The Rotor-Gene ScreenClust HRM Software is intended for molecular biology applications. This product is not intended for the diagnosis, prevention, or treatment of a disease.

Identification of a class IV SNP.

A human A/T SNP in the AHRR7 gene was analyzed using genomic DNA from wild-type (blue), homozygous mutant (green), and heterozygous (red) samples. Experiments were performed using the Type-it HRM PCR Kit and a Rotor-Gene Q cycler with a HRM channel. Data analysis was performed with the unsupervised mode of Rotor-Gene ScreenClust HRM Software. A/T polymorphisms (class IV SNPs) are most difficult to discriminate due to minute differences between homozygote alleles (in this example, less than 0.1°C). [A] HRM raw data, [B] the normalized melting curve, [C] the residual plot, and [D] the cluster plot are shown. All pseudo-unknowns were correctly clustered according to genotype.

These Rotor-Gene Q cycler, in combination with Rotor-Gene ScreenClust HRM Software, enables identification of even difficult class IV A/T SNPs which can have differences in melting temperatures as low as 0.1°C (see figure "Identification of a class IV SNP").

HRM is an innovative technique that characterizes double-stranded PCR products based on their melting (dissociation) behavior as they transition from double-stranded DNA (dsDNA) to single-stranded DNA (ssDNA) with increasing temperature. First, the target sequence is amplified by PCR to a high copy number. Next, high-precision melting of PCR products enables discrimination of samples according to sequence, length, GC content, or strand complementarity, down to single base-pair changes. No prior sequence information is needed, enabling detection of previously unknown and even complex sequence variations in a simple and straightforward way.

HRM data analysis discriminates between genotypes by comparing the position and shape of melting curves of different samples. Melting curves of heterozygotes and homozygotes differ in their shapes and melting points (Tm). In standard HRM software packages, variations in melt curve shape and position compared to a control are used to differentiate between samples. This method can cause unreliable, difficult-to-interpret results, and time-consuming manual data interpretation may be necessary. In contrast, Rotor-Gene ScreenClust HRM Software uses innovative mathematical algorithms to characterize samples and group them into clusters.

Procedure

Rotor-Gene ScreenClust HRM Software analyzes HRM data in 4 steps:

Normalization

Generation of a residual plot

Principal component analysis

Clustering

The software guides the user through all the steps, giving information about any choices that can be made at each step.

HRM performed on the Rotor-Gene cycler produces raw data (*.rex files) that can be further analyzed using Rotor-Gene ScreenClust HRM Software. In the first step in analysis, raw data are normalized by applying curve scaling to a line of best fit so that the highest fluorescence value is equal to 100 and the lowest is equal to zero. Next, the curves are differentiated and a composite median curve is constructed using the median fluorescence of all samples. The melt traces for each sample are subtracted from this composite median curve to draw a residual plot. The individual sample characteristics are extracted by principal component analysis from the residual plot. Principal component analysis is a well-established method of data analysis. However, Rotor-Gene ScreenClust HRM Software is the first software application to apply principal component analysis to HRM data. Principal component analysis highlights similarities and differences in the data and is used to create a cluster plot in supervised or unsupervised mode (see figure "Identification of a class IV SNP"). Clustering (grouping) of data is performed according to allele.

Supervised mode is often used for SNP genotyping, where the genotypes are known. In supervised mode, the user assigns one or more control samples for each cluster and the software classifies (autocalls) all unknown samples to clusters according to their characteristics. The unsupervised mode is used to find new mutations in the data when there is no prior knowledge or only partial knowledge of the genotypes present in the samples. In unsupervised mode, the software calculates the optimum number of clusters by itself. This feature is an excellent tool for the discovery of new polymorphisms.

The result of analysis in both modes is displayed as an easy-to-interpret cluster plot (see figure "Identification of a class IV SNP"). Statistical probabilities and typicalities are provided to allow easy comparison of results from different experiments. All data and graphs can be conveniently exported in various formats such as JPG, PDF, CSV, or XLS file formats and are summarized in an experimental report.

Applications

HRM analysis using Rotor-Gene ScreenClust HRM Software provides enormous potential for a wide range of applications. SNP genotyping, and mutation scanning or detection experiments can especially benefit from the power of this technology.